What are the hardware requirements for Llama 3.1 SauerkrautLM 70B Instruct AWQ?

Llama 3.1 SauerkrautLM 70B Instruct AWQ requires approximately 39.9 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3.1 SauerkrautLM 70B Instruct AWQ and how large is it?

Llama 3.1 SauerkrautLM 70B Instruct AWQ is developed by WisPerMed, a model with 70b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Llama 3.1 SauerkrautLM 70B Instruct AWQ?

Llama 3.1 SauerkrautLM 70B Instruct AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.1 SauerkrautLM 70B Instruct AWQ by WisPerMed — VRAM 39.9GB, 128K context

Name: Llama 3.1 SauerkrautLM 70B Instruct AWQ
Author: WisPerMed

Llama 3.1 SauerkrautLM 70B Instruct AWQ is an open-source language model by WisPerMed. Features: 70b LLM, VRAM: 39.9GB, Context: 128K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.15.

4-bit Awq Base model:quantized:vagosolut... Base model:vagosolutions/llama... Conversational De En Instruct Llama Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/WisPerMed/Llama-3.1-SauerkrautLM-70b-Instruct-AWQ

Llama 3.1 SauerkrautLM 70B Instruct AWQ Parameters and Internals

LLM Name	Llama 3.1 SauerkrautLM 70B Instruct AWQ
Repository 🤗	https://huggingface.co/WisPerMed/Llama-3.1-SauerkrautLM-70b-Instruct-AWQ
Base Model(s)	VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct
Model Size	70b
Required VRAM	39.9 GB
Updated	2026-07-28
Maintainer	WisPerMed
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-9 4.9 GB: 2-of-9 4.9 GB: 3-of-9 4.9 GB: 4-of-9 4.9 GB: 5-of-9 4.9 GB: 6-of-9 4.9 GB: 7-of-9 3.4 GB: 8-of-9 2.1 GB: 9-of-9
Supported Languages	de en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.45.2
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	float16

Rank the Llama 3.1 SauerkrautLM 70B Instruct AWQ capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Llama 3.1 SauerkrautLM 70B Instruct AWQ

Best Alternatives	Context / RAM	Downloads	Likes
...0B Instruct Gradient 1048K AWQ	1024K / 39.9 GB	5	1
...70B Instruct Gradient 262K AWQ	256K / 39.9 GB	5	0
Llama 3.3 70B Instruct AWQ	128K / 39.9 GB	262002	46
...lama 3.3 70B Instruct AWQ INT4	128K / 39.9 GB	82744	30
Llama 3.3 70B Instruct AWQ	128K / 39.9 GB	16090	12
Llama 3 70B Instruct AWQ	8K / 39.9 GB	122175	71
Meta Llama 3 70B Instruct AWQ	8K / 39.9 GB	8	1
Llama 3 70B Instruct AWQ	8K / 39.9 GB	17	1
... Llama 3 70B Instruct AWQ GEMM	8K / 39.9 GB	6	0
... Llama 3 70B Instruct AWQ GEMM	8K / 39.9 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than WisPerMed/Llama-3.1-SauerkrautLM-70b-Instruct-AWQ.